Predicting non-life insurer's insolvency using non-kernel fuzzy quadratic surface support vector machines

Abstract

Due to the serious consequence caused by insurers' insolvency, how to accurately predict insolvency becomes a very important issue in this area. Many methods have been developed to do this task by using some firm-level financial information. In this paper, we propose a new approach which incorporates several macroeconomic factors in the model and applies feature selection to eliminate the bad effect of some unrelated variables. In this way, we can obtain a more comprehensive and accurate model. More importantly, our method is based on the state-of-the-art non-kernel fuzzy quadratic surface support vector machine (FQSSVM) model which not only performs superiorly in prediction, but also becomes very applicable to the users. Finally, we conduct some numerical experiments based on the real data of non-lifer insurers from USA to show the predictive power and efficiency of our proposed method compared with other benchmark methods. Specifically, in a reasonable computational time, FQSSVM has the most accurate prediction rate and least Type I and Type II errors.

Kernel-free double well potential support vector regression

“…Yang et al [26] proposed the QSVC model with non-convex bounded ramp loss function. Tian et al [19] proposed the kernel-free fuzzy QSVC for predicting the bankruptcy of non-life insurance companies. Next, Gao et al [6,7] proposed two kernel-free quartic surface support vector classification (DWPSVC) for the binary and multi-class classification problems, which further improved the classification accuracy.…”

Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm

“…The classical support vector machine algorithm has been used to the stock market forecasting [7] and financial distress [8] or bankruptcy prediction [9] . As compared to the logistic and decision tree models, the most accurate classification is usually obtained using a classical support vector machines model.…”